Academic Journal
Prediction of glycaemic control and quality of life in people with type 2 diabetes using glucose‐lowering drugs with machine learning—The Maastricht study
| Τίτλος: | Prediction of glycaemic control and quality of life in people with type 2 diabetes using glucose‐lowering drugs with machine learning—The Maastricht study |
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| Συγγραφείς: | Nikki C. C. Werkman, Johannes T. H. Nielen, José Tapia‐Galisteo, Francisco J. Somolinos‐Simón, Maria Elena Hernando, Junfeng Wang, Li Jiu, Wim G. Goettsch, Hans Bosma, Miranda T. Schram, Marleen M. J. van Greevenbroek, Anke Wesselius, Coen D. A. Stehouwer, Johanna H. M. Driessen, Gema Garcia‐Sáez |
| Πηγή: | Diabetes, Obesity and Metabolism. 27:5524-5537 |
| Στοιχεία εκδότη: | Wiley, 2025. |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | antidiabetic drug, glycaemic control, IDENTIFICATION, SDG 3 - Good Health and Well-being, observational study, type 2 diabetes, DEPRESSION |
| Περιγραφή: | BackgroundDespite the heterogeneity of type 2 diabetes (T2D), all patients are treated according to the same guideline. Some people have more difficulty reaching treatment goals (adequate glycaemic control) and maintaining quality of life (QoL). Therefore, a prediction model identifying who is unlikely to reach these goals within the next year would be useful to allow specific attention to these people.AimTo investigate if machine learning algorithms can predict which individuals are unlikely to reach glycaemic control and likely to deteriorate in QoL in 1 year.MethodsWe used data from The Maastricht Study, including 842 people with T2D and information on HbA1c values, and 964 people with T2D and information on QoL. We evaluated several machine learning algorithms with feature selection methods and hyperparameter tuning in fivefold cross‐validation for the corresponding outcomes.ResultsThe prediction of inadequate glycaemic control showed good performance. The support vector machine classifier performed best in terms of accuracy (0.76 (95% CI 0.71–0.79)), precision (0.79 (95% CI 0.71–0.83)) and area under the receiver operating characteristic curve (AUC‐ROC) (0.85 (95% CI 0.80–0.89)). The multi‐layer perceptron classifier performed best in terms of recall (0.72 (95% CI 0.64–0.79)) and F1‐score (0.73 (95% CI 0.64–0.79)). The prediction of deterioration in QoL showed inadequate performance and did not seem feasible.ConclusionPrediction of glycaemic control after 1 year in T2D is feasible with good model performance. However, the prediction of deterioration in QoL remains a challenge and needs further work. |
| Τύπος εγγράφου: | Article |
| Γλώσσα: | English |
| ISSN: | 1463-1326 1462-8902 |
| DOI: | 10.1111/dom.16598 |
| Σύνδεσμος πρόσβασης: | https://pure.eur.nl/en/publications/b8e3c63d-2faa-42ca-9d0b-c354374fdb54 https://doi.org/10.1111/dom.16598 https://cris.maastrichtuniversity.nl/en/publications/2296760d-06ff-437a-8857-cbfb24bfdd64 https://doi.org/10.1111/dom.16598 https://research-portal.uu.nl/en/publications/e35a4435-27b7-4161-bdad-252bb3293baa https://doi.org/10.1111/dom.16598 |
| Rights: | CC BY NC ND |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....84e4e63e1ca4665cb307be46e507f1c1 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 14631326 14628902 |
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| DOI: | 10.1111/dom.16598 |